{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "sb.set_context('paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': 6797089423601767780,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n",
      "inner join with pdl1: 26 to 26 rows\n"
     ]
    }
   ],
   "source": [
    "from utils import data\n",
    "from utils.paper import mann_whitney_hyper_label_printer, fishers_exact_hyper_label_printer\n",
    "cohort = data.init_cohort(exclude_patient_ids=set(), only_patients_with_bams=True)\n",
    "cohort_df = cohort.as_dataframe(join_with=['pdl1'])\n",
    "cohort_df['Response'] = cohort_df['benefit'].map(lambda v: cohort.benefit_plot_name if v\n",
    "                                                 else 'No ' + cohort.benefit_plot_name)\n",
    "\n",
    "\n",
    "annotated_effects = cohort.load_polyphen_annotations(as_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyensembl import ensembl75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gene_name</th>\n",
       "      <th>index</th>\n",
       "      <th>TSPAN6</th>\n",
       "      <th>TNMD</th>\n",
       "      <th>DPM1</th>\n",
       "      <th>SCYL3</th>\n",
       "      <th>C1orf112</th>\n",
       "      <th>FGR</th>\n",
       "      <th>CFH</th>\n",
       "      <th>FUCA2</th>\n",
       "      <th>GCLC</th>\n",
       "      <th>...</th>\n",
       "      <th>RPS9</th>\n",
       "      <th>PCDHB19P</th>\n",
       "      <th>PRPF31</th>\n",
       "      <th>MBOAT7</th>\n",
       "      <th>LDHAL6FP</th>\n",
       "      <th>AC005795.1</th>\n",
       "      <th>PRKCD</th>\n",
       "      <th>OR6R2P</th>\n",
       "      <th>TSEN34</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>540.2140</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>649.00029</td>\n",
       "      <td>830.8775</td>\n",
       "      <td>582.19160</td>\n",
       "      <td>786.00010</td>\n",
       "      <td>454.547440</td>\n",
       "      <td>4631.10524</td>\n",
       "      <td>748.34841</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.060</td>\n",
       "      <td>609.9150</td>\n",
       "      <td>289.089618</td>\n",
       "      <td>558.051916</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>3.55145</td>\n",
       "      <td>2297.733000</td>\n",
       "      <td>1.00419</td>\n",
       "      <td>121.806427</td>\n",
       "      <td>0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>471</td>\n",
       "      <td>1133.3780</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>445.99969</td>\n",
       "      <td>903.3402</td>\n",
       "      <td>456.25392</td>\n",
       "      <td>342.99990</td>\n",
       "      <td>984.792258</td>\n",
       "      <td>1619.13800</td>\n",
       "      <td>989.51800</td>\n",
       "      <td>...</td>\n",
       "      <td>3040.690</td>\n",
       "      <td>107.7640</td>\n",
       "      <td>195.470580</td>\n",
       "      <td>505.955050</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>6.58465</td>\n",
       "      <td>1150.999900</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>315.858201</td>\n",
       "      <td>0471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>522</td>\n",
       "      <td>1182.8780</td>\n",
       "      <td>19.00004</td>\n",
       "      <td>1216.99897</td>\n",
       "      <td>2934.5000</td>\n",
       "      <td>1905.22708</td>\n",
       "      <td>2080.99702</td>\n",
       "      <td>3423.509560</td>\n",
       "      <td>2694.14115</td>\n",
       "      <td>5283.14540</td>\n",
       "      <td>...</td>\n",
       "      <td>2943.530</td>\n",
       "      <td>21.2127</td>\n",
       "      <td>195.200002</td>\n",
       "      <td>294.738990</td>\n",
       "      <td>17.5000</td>\n",
       "      <td>6.08811</td>\n",
       "      <td>2669.242960</td>\n",
       "      <td>1.01251</td>\n",
       "      <td>4.892077</td>\n",
       "      <td>0522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1233</td>\n",
       "      <td>201.0806</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>225.99970</td>\n",
       "      <td>616.9180</td>\n",
       "      <td>776.95600</td>\n",
       "      <td>945.99969</td>\n",
       "      <td>1183.850300</td>\n",
       "      <td>710.33517</td>\n",
       "      <td>1073.24268</td>\n",
       "      <td>...</td>\n",
       "      <td>2863.210</td>\n",
       "      <td>175.2840</td>\n",
       "      <td>123.326675</td>\n",
       "      <td>267.840510</td>\n",
       "      <td>15.7641</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>774.582010</td>\n",
       "      <td>2.12905</td>\n",
       "      <td>194.579000</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1249</td>\n",
       "      <td>312.2663</td>\n",
       "      <td>163.00000</td>\n",
       "      <td>102.99996</td>\n",
       "      <td>204.7884</td>\n",
       "      <td>498.61147</td>\n",
       "      <td>107.99996</td>\n",
       "      <td>733.000220</td>\n",
       "      <td>511.54965</td>\n",
       "      <td>356.15960</td>\n",
       "      <td>...</td>\n",
       "      <td>547.199</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>54.899960</td>\n",
       "      <td>22.543993</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>14.53360</td>\n",
       "      <td>50.999949</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.626220</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39295 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gene_name index     TSPAN6       TNMD        DPM1      SCYL3    C1orf112  \\\n",
       "0            40   540.2140   56.00000   649.00029   830.8775   582.19160   \n",
       "1           471  1133.3780   77.00000   445.99969   903.3402   456.25392   \n",
       "2           522  1182.8780   19.00004  1216.99897  2934.5000  1905.22708   \n",
       "3          1233   201.0806   24.00000   225.99970   616.9180   776.95600   \n",
       "4          1249   312.2663  163.00000   102.99996   204.7884   498.61147   \n",
       "\n",
       "gene_name         FGR          CFH       FUCA2        GCLC     ...      \\\n",
       "0           786.00010   454.547440  4631.10524   748.34841     ...       \n",
       "1           342.99990   984.792258  1619.13800   989.51800     ...       \n",
       "2          2080.99702  3423.509560  2694.14115  5283.14540     ...       \n",
       "3           945.99969  1183.850300   710.33517  1073.24268     ...       \n",
       "4           107.99996   733.000220   511.54965   356.15960     ...       \n",
       "\n",
       "gene_name      RPS9  PCDHB19P      PRPF31      MBOAT7  LDHAL6FP  AC005795.1  \\\n",
       "0          2019.060  609.9150  289.089618  558.051916    5.5000     3.55145   \n",
       "1          3040.690  107.7640  195.470580  505.955050    6.0000     6.58465   \n",
       "2          2943.530   21.2127  195.200002  294.738990   17.5000     6.08811   \n",
       "3          2863.210  175.2840  123.326675  267.840510   15.7641     0.00000   \n",
       "4           547.199    2.5000   54.899960   22.543993    9.0000    14.53360   \n",
       "\n",
       "gene_name        PRKCD   OR6R2P      TSEN34  patient_id  \n",
       "0          2297.733000  1.00419  121.806427        0040  \n",
       "1          1150.999900  1.00000  315.858201        0471  \n",
       "2          2669.242960  1.01251    4.892077        0522  \n",
       "3           774.582010  2.12905  194.579000        1233  \n",
       "4            50.999949  0.00000    6.626220        1249  \n",
       "\n",
       "[5 rows x 39295 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.data import patient_id_to_str\n",
    "kallisto_data = pd.read_csv('../bladder-kallisto-tximport.csv', index_col=0)\n",
    "kallisto_data['gene_name'] = kallisto_data.index.map(lambda gid: ensembl75.gene_name_of_gene_id(gid))\n",
    "kallisto_data = kallisto_data.reset_index().drop(['index'], axis=1).set_index('gene_name').T.reset_index()\n",
    "kallisto_data['patient_id'] = kallisto_data['index'].map(patient_id_to_str)\n",
    "kallisto_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Print mutated\n",
    "# Plot Fisher's Exact for mutated vs not, benefit vs not\n",
    "# Mann - Whitney \n",
    "# AUC with gene expression\n",
    "\n",
    "from cohorts.plot import mann_whitney_plot, fishers_exact_plot, roc_curve_plot\n",
    "\n",
    "def create_gene_summary(gene_name):\n",
    "    mutations = annotated_effects[annotated_effects.gene == gene_name]\n",
    "    expression = kallisto_data.set_index('patient_id')[[gene_name]].sum(axis=1).reset_index()\n",
    "    expr_col = gene_name + ' scaledTPM'\n",
    "    expression.rename(columns={0 : expr_col}, inplace=True)\n",
    "    df = cohort_df.copy()\n",
    "    \n",
    "    mut_col = gene_name + ' Mutated'\n",
    "    df[mut_col] = df.patient_id.isin(mutations.patient_id)\n",
    "    \n",
    "    df = df.merge(expression, how='left')\n",
    "    \n",
    "    if len(mutations) > 0:\n",
    "        cols = 3\n",
    "        width = 10\n",
    "        height = 5\n",
    "    else:\n",
    "        cols = 2\n",
    "        width = 8\n",
    "        height = 4\n",
    "    f, ax = plt.subplots(1, cols, figsize=(width, height))\n",
    "    \n",
    "    \n",
    "    if len(mutations) > 0:\n",
    "        mut_plot = fishers_exact_plot(data=df, condition1='Response', condition2=mut_col, ax=ax[0])\n",
    "        mut_plot.plot.set_ylim(0, 1)\n",
    "        fishers_exact_hyper_label_printer(mut_plot, gene_name)\n",
    "        \n",
    "  \n",
    "    \n",
    "    expr_plot = mann_whitney_plot(data=df, \n",
    "                                  condition='Response', \n",
    "                                  distribution=expr_col, \n",
    "                                  condition_value=cohort.benefit_plot_name, \n",
    "                                  ax=ax[-2])\n",
    "    \n",
    "    mann_whitney_hyper_label_printer(expr_plot, gene_name)\n",
    "  \n",
    "    expr_roc = roc_curve_plot(data=df, value_column=expr_col, outcome_column='benefit', ax=ax[-1])\n",
    "    f.tight_layout()\n",
    "    \n",
    "    return mutations[['patient_id', 'aa_change', 'hvar_pred', 'hvar_prob', 'hdiv_pred']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=98.0, p-value=0.257704686002 (two-sided)\n",
      "{{{HLA-A_plot}}}\n",
      "{{{HLA-A_benefit:25457.41 (range 1923.57-82781.62)}}}\n",
      "{{{HLA-A_no_benefit:9918.91 (range 596.29-62547.18)}}}\n",
      "{{{HLA-A_mw:n=26, Mann-Whitney p=0.26}}}\n",
      "HLA-A scaledTPM, Bootstrap (samples = 100) AUC:0.627325081602, std=0.135227549524\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>aa_change</th>\n",
       "      <th>hvar_pred</th>\n",
       "      <th>hvar_prob</th>\n",
       "      <th>hdiv_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, aa_change, hvar_pred, hvar_prob, hdiv_pred]\n",
       "Index: []"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oPOsI8aopir/7Bg0aKLa2tsqdO3fU65OiKMrly5cVGxsb5ZtvvtG7rdWrVyt2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\nEAShShPJiiAIgiAIVdr/A/h8xnTmQvEhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa891e0ced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gene_summary('HLA-A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=94.0, p-value=0.359538043823 (two-sided)\n",
      "{{{HLA-B_plot}}}\n",
      "{{{HLA-B_benefit:10565.97 (range 1945.90-67052.75)}}}\n",
      "{{{HLA-B_no_benefit:7720.74 (range 391.03-80578.17)}}}\n",
      "{{{HLA-B_mw:n=26, Mann-Whitney p=0.36}}}\n",
      "HLA-B scaledTPM, Bootstrap (samples = 100) AUC:0.610300052256, std=0.12094117361\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>aa_change</th>\n",
       "      <th>hvar_pred</th>\n",
       "      <th>hvar_prob</th>\n",
       "      <th>hdiv_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, aa_change, hvar_pred, hvar_prob, hdiv_pred]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIwtc+EuI/CQkJGBubp6rvEKFCgCv5O3Fonygb926FXNzc3744QcyMjIKrT9w4EDOnj3L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8d16e6c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gene_summary('HLA-B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=99.0, p-value=0.235729052816 (two-sided)\n",
      "{{{HLA-C_plot}}}\n",
      "{{{HLA-C_benefit:15423.64 (range 1690.11-52575.19)}}}\n",
      "{{{HLA-C_no_benefit:8102.90 (range 327.97-53816.39)}}}\n",
      "{{{HLA-C_mw:n=26, Mann-Whitney p=0.24}}}\n",
      "HLA-C scaledTPM, Bootstrap (samples = 100) AUC:0.654323353456, std=0.114955915318\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>aa_change</th>\n",
       "      <th>hvar_pred</th>\n",
       "      <th>hvar_prob</th>\n",
       "      <th>hdiv_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, aa_change, hvar_pred, hvar_prob, hdiv_pred]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i20H+Hf7n+fPnPHv2TKft4cOHOklLSEgIgGxiKD4IiTExtDp8OK/DyHV67bp8\n+PBhNmzYQHBwMMnJyQAUL16cZs2a5WhwInd5eXnldQgC+Xd42cqVK1m8eLH6s6Ioab7vaDQaSpUq\nlZuhCSFyUbrJyuPHj9myZQubN28mIiJC/SbTsGFDPD09adWqFfny6ZXrCCHEa3v10k96l4I6dOiQ\nG+EIIfJAutlGs2bNSEpKQlEUzM3N6dy5Mz169OCjjz7KxfCEEB8yGxsbNQnZuXMnGo2G9u3bq49r\nNBqKFClC7dq1adu2bV6FKYTIYekmK4mJiWg0GjQaDUlJSWzfvp3t27en2Vej0XDixIkcC1II8WFq\n3bo1rVu3BlKSFYBZs2blZUhCiDyg13Wc58+fq3/XlrZO72chhMgJf/75Z16HIITIIxkmK+ldG5by\n+kKI3KYtVHnkyBFu3bpFbGxsqj4DBgzIg8iEEDkt3WRFezugEEK8DZ48eUKvXr0IDQ1Nt48kK0K8\nn9KtYJvTjhw5wmeffYaDgwN169ala9eunD59Wn08OjqacePG0bBhQxwcHOjduzfXr19PNU58fDwz\nZ87E0dERe3t7PD09OXv2bKp+iqLg6+uLs7MztWrVwsPDg3379qUZ2+bNm2nbti01a9bE1dWVjRs3\nZt8TF0K8loULF3Ljxo1s20z177//ZsiQIdSrV4+6devy1VdfERERodexERERjBkzhhYtWmBvb4+L\niwvz5s3jxYsXWY5DCJG5DC8DhYeHs3TpUi5evAhA7dq1+eKLLyhXrtwbnXTjxo1MmTKFzz//nC+/\n/JLk5GSuXr2qM63bv39/IiIiGD9+PGZmZvj6+uLl5UVAQAClS5dW+/n4+HDs2DFGjx6NlZUV69at\no2/fvmzatAlbW1u137x581i5ciUjRoygevXq7N69m6FDh+Lr60vTpk3Vfps3b2bChAkMGDCARo0a\ncfLkSSZNmgSAp6fnGz1vIcTrCw4ORqPR4OHhwfbt29FoNIwZM4b169djYGBA37599R4rNjYWLy8v\n8ufPz/fffw/A3Llz6dWrFzt27KBAgQLpHvvixQu8vb1JSkpi2LBhWFpacunSJRYsWMCdO3eYM2fO\nGz9XIcQrlHSEh4crDRs2VGxtbXX+fPzxx8rdu3fTOyxT4eHhSq1atZQ1a9ak22f//v2Kra2tcubM\nGbUtJiZGadCggTJlyhS17erVq4qNjY3i7++vtiUmJiouLi7KwIED1bbHjx8rdnZ2ysKFC3XO06tX\nL8Xd3V3L2OdgAAAgAElEQVTn2EaNGiljx47V6efj46M0bNhQSUxMTDfmu3fvKlWrVn2j10aI90VO\n/D7Y2dkptra2yqNHjxQbGxvF1tZWURRFCQkJUWxsbJRly5bpPdaqVauU6tWrK3fu3NGJuXr16srK\nlSszPDY4OFixtbVVjh8/rtP+ww8/KDVq1FBiY2PTPE7eI0R22N+sWV6HkC2y+vuQ7mWgxYsX8/Tp\n01RTrVFRUSxZsuS1k6OtW7diYGBAjx490u1z6NAhSpUqRf369dW2QoUK0aJFC4KCgtS2oKAgjIyM\ndOorGBoa4ubmRnBwMAkJCQAcPXqUxMRE3N3ddc7j7u7O9evXuXfvHgDnz5/n6dOnqfp5eHgQGRnJ\nuXPnXvt5CyHejLacftGiRdWClE+ePFFnerNyufbQoUPY29vrzBJbWVlRp04dnfeYtGjfVwoVKqTT\nXrhwYZKTk+UGBCFyQLrJysmTJ9FoNNSuXZuFCxcyf/58ateujaIonDx58rVP+Pvvv1OpUiV2795N\n69atqVGjBm3atGHdunVqn9DQUKpUqZLqWGtrayIiItTrwmFhYVhZWZE/f/5U/RISErhz547az9jY\nmPLly6fqpyiKumBP+99Xz12lShWdfkKI3Gdubg5ATEwMJUqUAGD06NGMGjUKgMjISL3Hyug9Jiws\nLMNjGzduTIUKFZg1axZhYWH8+++/nDx5kjVr1vDJJ59keAlJCPF60l2z8s8//wApaz0sLCwAqFmz\nJs7Ozupjr+Phw4c8fPiQWbNmMWLECMqVK8fevXv57rvvSE5O5vPPPycyMhIrK6tUx2rfrKKjozEx\nMSEqKkpte1mRIkWA/715RUVFUTiNXSW1/aKionT+a2ZmluZ5tY8LIXJfpUqVePjwIXfv3qVevXrs\n2rWL48ePAymFKatXr673WJGRkWm+d5ibmxMdHZ3hscbGxqxfv56vvvoKNzc39fzdunXj22+/zcIz\nEkLoK9MKttpEBaBMmTIAJCUlvfYJk5OT+ffff5k5cyatWrUC4OOPPyY8PBxfX18+//zz1x5bCPH+\n6tq1K2XLliU2NpbBgwcTHBysfiEpUqQIPj4+uRJHfHw8Q4cO5fHjx/zwww9YWFhw6dIlFi1ahIGB\nARMnTsyVOIT4kGRawfbs2bNpXoN9tf3l9SUZKVq0KHfu3KFx48Y67U2aNCE4OJhHjx5hbm6e5izG\nqzMfZmZm3L9/P1W/l9/AtP1iYmLS7af9hqUdNzo6Wp1mfvm8aX0TE0LkDjc3N3UmA2Dfvn2cPHkS\nQ0ND6tWrp/6+6yOj95hXZ1ZftWXLFs6ePcu+ffvUNS/16tWjUKFCjB8/nk8++QQbGxu9YxFCZC7T\nZOXVmQ5taf2X2zUaDVeuXNHrhNbW1ly4cCHTPmntNRQWFoalpSUmJiZqvwMHDhAXF6ezbiU0NBQj\nIyN1jYq1tTXx8fHcvXtXZ0FdaGgoGo0Ga2tr4H9rU27cuKGTrGjXqmj7CSHynpmZGS4uLkDKotdV\nq1bh7e2t17HW1tZprkELDQ2lcuXKGR57/fp1zMzMUpVwqFmzJoqiEBYWJsmKENks06Jwr94NlN4f\nfWk3JQsODtZpP3bsGBYWFpQoUQJnZ2cePHigU9zt2bNnHDx4kJYtW6ptzs7OJCQkEBgYqLYlJSUR\nGBiIo6MjRkZGADRt2hRDQ0N27Nihc84dO3ZQpUoVypYtC6TUkSlatKi6YZpWQEAARYoUoU6dOno/\nTyFE9nn8+DGXL19OVbRNURR++eUXXFxcmDlzpt7jOTs7c+HCBcLDw9W28PBwzp8/r/Mek5aSJUsS\nHR3N3bt3ddovXLiARqPRqQMlhMge6c6s6HtZJ6uaNWtGgwYNGD9+vHrbYWBgICdOnGD69OkAtGzZ\nEnt7e0aNGsWoUaMoXLgwfn5+APznP/9Rx6pWrRrt2rVj+vTpJCQkYGVlxYYNG7h3755OYaZixYrR\nu3dv/Pz8MDU1VYvCnTlzhqVLl6r98uXLx9ChQ5k8eTKlSpWicePGnDx5En9/f7799lv1dkkhRO6I\nj49nzJgx7N27V21zcHBg/vz5JCUlMWTIEC5dupTlDVW7d+/O+vXrGTRoEEOHDgVgwYIFlClTRqes\nwv3792nVqhWDBw9m0KBBAHTq1IlVq1bRr18/BgwYoBaFW7p0KXZ2dtStWzebnr0QQivdT9+1a9fm\n2EmXLFnCnDlzWLRoEVFRUVSqVInZs2fTrl07IOWykp+fHzNnzmTSpEnEx8fj4ODA2rVrU31rmTFj\nBnPnzmX+/PnExMRga2vL8uXLdarXAowYMQJTU1PWrFnDo0ePqFixIvPnz6dZs2Y6/Tw9PTEwMGDF\nihWsWLECS0tLxo8fL9VrhcgDfn5+OjOnkFIPafTo0bx48UKtrg0p60b0ZWJiwurVq5k2bRpjxoxB\nURQaN26Mj4+PepkZSHP2uGzZsmzatIlFixYxf/58nj59ioWFBZ6enrI3kdBxuEMHEtNYL/km8qVx\nZ+uHQKOkcw3Hx8cHjUbDtGnTcjumd1J4eDgtW7YkKCgozduuhfiQZNfvQ4cOHbhx4wYGBgap6iJB\nSjJhY2PDiBEjUn3xeNvIe8SH50Dz5rQ6fDivw3grZfX3Id2ZFX9/f0lWhBB56u7du2g0GpYuXaom\nI4cPH2bAgAFoNBq8vLwYO3YsBgZ5tierECIXyG+4EOKtpd3c1NHRUW1zcnJS/z5kyBBJVIT4AMiK\nUSHEW+/ixYtp3nV448YNnXa5Y0+I91OmyYqXl1emg2g0GlavXp0tAQkhxKt69uyp87P2zp+X27NS\n70kI8W7JNFn57bffMnw8q7cMCiFEVslOxkJ82DJNVuRN4sMUHx/PpUuXKFasGBUrVszrcMQHysHB\nQb4MCSEyT1bWrFmTG3GIt8iDBw/w8fHh0aNHALRp04bBgwfncVTiQ7Rhw4a8DkEI8RbINFlp0KBB\nbsQh3iL+/v5qogIpG8a5u7urey0JIYQQuUnuBhKpREdHp2pLa4dakf0ePXpEYGAgcXFxtG7dmgoV\nKuR1SEIIkeckWRGpODs762w0aWlpSfXq1fMwog/D8+fPGTlyJE+ePAFg7969zJ07N9XuvkII8aFJ\nN1kJCgrKzTjEW6RevXpMnDiRw4cPU7RoUdzd3TE0NMzrsN56ERERPH/+/LWPP3v2rJqoQMoi523b\nttG+ffs3js3U1BRLS8s3HkcIIfJCusmK9pblzG5d1urYsWP2RCTeCnXq1JECW1kQFRVF//79SU5O\nfqNxXq3GeuDAAQ4cOPBGY2rHXbt2Lebm5m88lhBC5LZ0k5WxY8fqfcugRqORZEV80MzNzfH19X2j\nmZWkpCSWLl3KzZs31TG//vprChUq9MbxmZqaSqIihHhnZbhmJbMaKxqNRuqwCPH/suMyy+zZs9mz\nZw++vr74+PjIWqE0JCUlERISQmRkJE2aNMnrcIQQuSDdZOXVuhqbNm3i2bNntG7dGgsLC/7++2/2\n79+PiYlJqlLYQojXY2hoSLVq1QAwNjbO42jePvv27WPy5Mk8fvxYLa/fu3dv7t+/z4QJE2jcuHFe\nhyiEyAF6JSurVq3i0aNHrF69WqfuyqlTp/D29pY3VSFEjjt37hzDhw8nOTlZZ0bXycmJ77//nr17\n90qyIsR7Sq+91bVVbF+dkrazswNg48aN2RyWEELo8vX1JSkpKVVxwmbNmgFw/vz5vAhLCJEL9EpW\ntNVM582bx7NnzwB49uwZc+fO1XlcCCFyyoULF9BoNPj5+em0a5OXBw8e5EVYQohcoFdRuGrVqnHx\n4kXWrVvHunXrMDU1Ve960Gg0sghQCJHjtO85ZcuW1WmPiYkBIDY2NtdjEvo53KEDif//7/QhyVe4\ncF6H8N7QK1kZO3Ysffr04cWLFwDq7AqAiYkJY8eOzZnohBDi/5UqVYqIiAj++OMPnfZVq1YBULp0\n6TyISugjMSaGVocP53UY4h2m12UgBwcHtm7dSrt27ShevDiGhoaUKFECNzc3tm7dSu3atXM6TiHE\nB87R0RFFUfjyyy/VNjc3N3788Uc0Gg2Ojo55GJ0QIifpvTdQ5cqVmTNnTk7GIoQQ6Ro4cCC//vor\nUVFRasHKmzdvoigK5ubm9O/fP48jFELkFL1mVrQePXrEnj172LBhQ07FI4QQabK0tGT9+vU0bNhQ\nLUip0Who2LAh69atw8LCIq9DFELkEL1nVlasWMG8efNISEhAo9HwySef4O7uzo0bN5g7dy6urq45\nGacQQlC5cmVWrVrFv//+S2RkJEWKFKFgwYKvNdbff//NtGnTOHHiBIqi0LhxY7755hu9KxGHhYWx\nYMECTp8+zYsXL7C0tOTTTz/l888/f614hBDp02tm5cCBA3z//ffEx8ejKIpakOnTTz9FURTZofkd\ndufOHY4cOcLjx4/zOhQhMtSmTRt8fX158OABBQsWpEyZMq+dqMTGxuLl5cWtW7f4/vvvmTVrFrdv\n36ZXr1563VV06dIlunfvTkJCAlOnTuXHH3+kb9++JCUlvVY8QoiM6TWzsnr1ajQaDfXq1dPZhVm7\nL8fly5dzJjqRo/z9/Vm5ciUA+fLlY9y4cdStWzePoxIibXfu3GHevHksWLCAxo0b061bN5ydncmX\nT+8JYtWmTZu4d+8ee/fupVy5cgBUrVoVFxcXNm7ciLe3d7rHKorC2LFjadKkCQsWLFDbX67uLYTI\nXnrNrFy5cgWAH374Qadde4344cOH2RyWyGnx8fE6lYcTExNZv359HkYkRMaKFSuGoigkJSURHBzM\n0KFDcXJyYvr06Vy7di1LYx06dAh7e3s1UQGwsrKiTp06mc4Unzp1ips3b2aY0AghspdeyUpCQgIA\nRYsW1WnXVq5NTEzM5rBETktMTCQuLk6nTVt0S4i3UXBwMMuXL6dTp06YmpqiKApPnz5lzZo1dOzY\nka5du+o9VmhoKFWqVEnVbm1tTVhYWIbH/v7770DKpaQePXpgZ2dH48aNmTJlSqrfKSFE9tArWSlT\npgwAR48eVdsSExPVcvtWVlY5EJrISQULFsTJyUmnzcXFJY+iESJzBgYGNGnShOnTp3PixAkWLFhA\nmzZtMDQ0RFEU/vzzT73HioyMxNzcPFW7ubk50dHRGR778OFDFEVh+PDhODk5sXLlSvr168fWrVsZ\nOXJklp+XECJzel3sbdGiBStXrmTYsGFqW4MGDXjx4gUajQZnZ+ccC1DknCFDhmBra8utW7eoXbu2\nFNUS74x//vmH27dvc/v27Vxf1Kq9ZdrDw0Pdnb5+/fokJiYyZ84cbt68SaVKlXI1JiHed3olKwMH\nDuTAgQPcvXtXLcb077//AlCuXDn69euXcxGKHGNkZISbm1tehyGEXp48eUJgYCA7d+7kwoULarui\nKOTPn5/WrVvrPZa5uTlRUVGp2qOiojAzM8vw2CJFigDQuHFjnXZHR0dmz55NSEiIJCtCZDO9LgOZ\nmZmxadMmunfvTsmSJTE0NKRUqVJ0796djRs3ZvrLnZm+fftia2vL/Pnzddqjo6MZN24cDRs2xMHB\ngd69e3P9+vVUx8fHxzNz5kwcHR2xt7fH09OTs2fPpuqnKAq+vr44OztTq1YtPDw82LdvX5oxbd68\nmbZt21KzZk1cXV11FqMKIXKfk5MTU6ZM4cKFC2oJhRo1ajBhwgSCg4NT3QCQEWtra0JDQ1O1h4aG\nUrly5UyPFULkLr3v+StWrBiTJ0/O9gB27drFtWvX1Bmbl/Xv35+IiAjGjx+PmZkZvr6+eHl5ERAQ\noLNpmY+PD8eOHWP06NFYWVmxbt06+vbty6ZNm7C1tVX7zZs3j5UrVzJixAiqV6/O7t27GTp0KL6+\nvjRt2lTtt3nzZiZMmMCAAQNo1KgRJ0+eZNKkSQB4enpm+2sghMic9nJPsWLFcHd3p0uXLmkuktWH\ns7Mzs2bNIjw8XF1zFx4ezvnz5zNdd9K0aVOMjIwIDg6mefPmavvRo0fRaDTUrFnztWISQmRAyUOR\nkZFKkyZNlN27dys2NjbKvHnz1Mf279+v2NraKmfOnFHbYmJilAYNGihTpkxR265evarY2Ngo/v7+\naltiYqLi4uKiDBw4UG17/PixYmdnpyxcuFAnhl69einu7u46xzZq1EgZO3asTj8fHx+lYcOGSmJi\nYprP5e7du0rVqlWVu3fvZvFVEELXjRs3FDc3N+XGjRt5Hcpry4nfh/79+yv79u1TEhIS3nisf//9\nV2nTpo3SoUMH5cCBA8qBAwcUd3d3pXXr1sq///6r9rt3755SrVo1ZfHixTrHL1y4UKlRo4YyZ84c\n5cSJE4qvr69Sq1YtxcfHJ91zfsjvEfubNcvrEMRbJqu/D+nOrFSrVk3vhEej0ai1WLLihx9+wMbG\nhnbt2jFixAidxw4dOkSpUqWoX7++2laoUCFatGhBUFAQ48aNAyAoKAgjIyPatm2r9jM0NFR3Y01I\nSMDIyIijR4+SmJiIu7u7znnc3d0ZN24c9+7do2zZspw/f56nT5+m6ufh4YG/vz/nzp2T4k9C5IFl\ny5Zl21gmJiasXr2aadOmMWbMGLXcvo+PDyYmJmo/5f8vNyn/X7Vba/DgwRQqVIgNGzawYsUKSpYs\nSb9+/Rg4cGC2xSiE+J90k5VXfzmz29mzZ9mxYwc7duxI8/GM6iAEBATw4sULTExMCAsLw8rKivz5\n86fql5CQwJ07d6hcuTJhYWEYGxtTvnz5VP0URSE0NJSyZcuq17FfPXeVKlXUfpKsCJE7li1bhkaj\noX///nolKwMGDNB7bAsLC50KtGkpW7YsV69eTfMxb29vKQwnRC5JN1l5eUYjuyUkJDBx4kT69u1L\nhQoV0uwTGRmZZv0WbW2E6OhoTExMiIqKSrNegnbFfmRkJJCyyr9w4cLp9tPeGaD976uLhrXnSOsO\nAiFEzpg3bx4GBgb079+fefPmpbm27WVZSVaEEO+OdJOVtWvX5thJf/zxR+Li4uSNRQiRqZdneTOa\n8c0skRFCvLuyvgPYG4qIiMDX15epU6cSFxdHXFyc+gYUHx9PTEwMpqamGdZBgP/NfJiZmXH//v1U\n/bQzKtqZEzMzM2JiYtLtp5050Y4bHR1NiRIlUp03rVkcIUTO0G60+erfRc453KEDiWm8V76JfGnM\naguRFXonK5GRkezYsYNbt26l2kJdo9Ewbdo0vca5e/cu8fHxjBo1SudbkkajYfny5axYsQJ/f3+s\nra05ceJEquPDwsKwtLRUF8FZW1tz4MAB4uLidNathIaGYmRkpK5Rsba2Jj4+nrt37+psXhYaGopG\no1FrJ2jXpty4cUMnWdGuZZEaC0LknkaNGqX5d5FzEmNiaHX4cF6HIYQOvZKVO3fu0LNnTx4/fpzq\nMeX/S0/rm6xUr16dNWvWpGr//PPP8fDwoFu3blSoUAFnZ2f8/f05e/Ys9erVA+DZs2ccPHhQ504d\nZ2dnFi5cSGBgIB07dgRS6jEEBgbi6OiIkZERkFIbwdDQkB07dvDll1+qx+/YsYMqVapQtmxZAGrX\nrk3RokXZuXOnzptjQEAARYoUoU6dOno9TyFE9qpRowYajYbLly+neqxPnz7qFx4hxPtHr2Rl4cKF\n6g7Lb6pQoULpLt4tU6aMmpi0bNkSe3t7Ro0axahRoyhcuDB+fn4A/Oc//1GPqVatGu3atWP69Okk\nJCRgZWXFhg0buHfvHnPmzFH7FStWjN69e+Pn54epqalaFO7MmTMsXbpU7ZcvXz6GDh3K5MmTKVWq\nFI0bN+bkyZP4+/vz7bffki9frl85E0KQ8iUkvXUpJ06ckDUrQrzH9Prk/e2333RuH9RoNCxZsgRf\nX18iIyP573//+8aBaDQanTcbjUaDn58fM2fOZNKkScTHx+Pg4MDatWt1qtcCzJgxg7lz5zJ//nxi\nYmKwtbVl+fLlOtVrAUaMGIGpqSlr1qzh0aNHVKxYkfnz59OsWTOdfp6enhgYGLBixQpWrFiBpaUl\n48ePl+q1QryFtJdoJVkR4v2lV7KinVXp06ePWuugRYsWVK1alZYtW3Ls2LE33rE3rVoGZmZmTJ06\nlalTp2Z4rLGxMWPGjGHMmDEZ9tNoNAwYMECvu5C6d+9O9+7dM+0nhMg5ixcv1pn5BLCzs9P5WVuG\n/+U1ZkKI94teGxlq130UKlQIY2NjAP7++29MTU0B2LlzZw6FJ4T4kCmKQmJiIomJial+1v7RLtRv\n0aJFXoYqhMhBes2sFC1alIiICCIjI7GwsODu3bv069dPTVzi4+NzNEghxIfJ0tJSXdT++++/o9Fo\ncHBwUB/XaDQUKVIEe3t7vLy88ipMIUQO0ytZqVKlChEREdy6dYvGjRuzceNGnevEdevWzdEghRAf\npi5dutClSxcAdQ3a+vXr8zIkIUQe0CtZ8fb2pm7dupiYmDB48GB+++03wsLCAKhcuXK2LLAVQoiM\n7Nu3L69DEELkEb2SlUaNGunUHNm1axfXrl3D0NCQSpUqYWhomGMBCvGhePbsGdu2bVN3ME9OTs7j\niPLe77//DkCdOnXUhf4ZlVGQOkhCvJ9eq2iIRqNJdVuwEOLNzJw5kwsXLgBgYGDAgQMHqFq1ah5H\nlbd69uyJgYEBV65coWfPnhnenqzRaNRETwjxftHrbqCvv/6aatWqsXjxYp32xYsXU61aNb7++usc\nCU6ID0VkZKSaqGhpZxU+dK9uZJjRHyHE+0mvmZXz588D6JS5B/Dw8GDhwoWcO3cu+yMT4gNSsGBB\nChYsyL///qu2yaaZ0L9/f3U2RXZpF+LDpVey8s8//wBQsmRJnXZtEaa09gwSQujP2NiY3r174+vr\nq9YOcXNzy+uw8tzw4cPVvw8bNiwPIxFC5CW9LgNpdzPWzrBoaX8uUKBANoclxIfHxcWF5cuXM2jQ\nIBRFUXcMF+m7fPky+/fvly9MQrzn9EpWqlatiqIo+Pj4EBAQwOXLlwkICOCbb75Bo9F88IsAhcgu\nRYsWxdraOq/DeCutWrUKT09PVq1aBcDUqVPp1q0bQ4YMwcXFJc0tO4QQ7we9kpVOnToB8ODBA8aO\nHUu3bt0YO3YsEREROo8LIUROCQoK4sKFC3z00Uc8ffqU9evXqwtrnz17luoGACHE+0OvZKVbt260\nadMmzZX3Li4udO3aNUeDFEKIW7duAVCtWjUuXrxIUlISjo6ODB48GEh9mVoI8f7Qu87KggUL2LNn\nD4cOHeLx48cUL14cZ2dn2rZtm5PxCSEEAFFRUQAUL16csLAwNBoNHh4etGnThkWLFqmPCyHeP1kq\nCteuXTvatWuXU7EIIUS6TE1NiYqKIiQkRJ1FqVChAgkJCQCYmJjkZXhvtcMdOpAYE6NX33yFC+dw\nNEJknV7Jyu3bt/nrr78oXbo0tra2hISE8MMPPxAREYGTkxOjRo2SkvtCiBxVqVIlzp8/T7du3YCU\n271tbGy4ffs2AKVKlcrSeH///TfTpk3jxIkTKIpC48aN+eabb7C0tMzSOH5+fsyZM4e6deuybt26\nLB2bWxJjYmh1+HBehyHEa9NrzcrChQsZMGAAp06dQlEUBg0axPHjxwkLC2P16tX4+fnldJxCiA+c\nl5cX8L8qtp06dSJ//vwEBwcDYG9vr/dYsbGxeHl5cevWLb7//ntmzZrF7du36dWrF7GxsXqPc/fu\nXZYuXarWnBJC5Ay9Zlb+/PNPAJo0acKVK1e4f/8+BQsWxNzcnIiICPbs2cPAgQNzNFAhxIfN1dWV\n0qVLc/bsWaysrHB1dQWgRo0aTJ8+nZo1a+o91qZNm7h37x579+6lXLlyQEqJBhcXFzZu3Ii3t7de\n40ycOBF3d3du3rwpG08KkYP0mlnRVrAtW7Ys169fB2DQoEGsXbsWSPl2IYQQOc3BwYF+/frRtm1b\ntQx/w4YN6dSpU5bq0xw6dAh7e3s1UQGwsrKiTp06BAUF6TXGzp07uXr1quyNJkQu0GtmJSkpCUiZ\nfg0NDVV3XS5dujQgW9kLIXJHXFwcP//8M0eOHOHJkycUK1aMFi1a8Omnn2JsbKz3OKGhobRs2TJV\nu7W1Nb/++mumx0dHRzNjxgxGjx6NmZlZlp6DECLr9EpWSpQowb179/Dx8VFX4VeqVEktcV20aNGc\ni1AIIfjfOpNLly6pbWFhYfz2228EBgaydu1adWuQzERGRqa5UaS5uTnR0dGZHj9z5kwqVqxIx44d\n9X8CQojXptdlICcnJxRFYf/+/fzzzz9UrFiRMmXKqOWtK1WqlKNBCiHETz/9xMWLF1MVp1QUhUuX\nLvHTTz/lShxnz55lx44dTJo0KVfOJ4TQM1kZMmQITZs2xcTEhKpVqzJjxgwA/vjjD8qXL0+LFi1y\nNEgh3lfR0dEEBAQQEBBAZGRkXofzVtu7dy8ajYbGjRuzbds2Tp48yS+//EKTJk1QFIXAwEC9xzI3\nN0+ziFxUVFSml3UmTJhA165dKVWqFDExMURHR5OUlERSUhIxMTHEx8dn+bkJITKm12WgokWLpnl7\n8vDhw3W2cBdC6C8mJoZhw4bx6NEjAPz9/Zk/f34eR/X20i7knzVrFsWLFwdS3ptmzJiBk5NTlhb6\nW1tbExoamqo9NDSUypUrZ3hsWFgYN2/eZMOGDakea9CgAT4+Pupt1kKI7JGlCrZCvE8ePnyo1/qE\nnHL8+HE1UQF48uQJW7dupWLFigA6azPeJmZmZlkuwJYdDAxSJoLj4uJ02rUzGdrH9eHs7MysWbMI\nDw/HysoKgPDwcM6fP8/IkSMzPFZ7F+TLpk6dSnJyMuPHj9e5w0gIkT0kWREfpIcPHzJgwIA8n7J/\n9QPW398fjUaDgYEBK1eu1Nk09G1hbGzMsmXLcj1h+eijjwgJCWHo0KF8+eWXWFpaEhERwdKlSwHU\nJE8f3bt3Z/369QwaNIihQ4cCKfuflSlThh49eqj97t+/T6tWrRg8eDCDBg0CoH79+qnGK1y4MMnJ\nyeQB8kQAACAASURBVNSrV+9NnqIQIh2SrIgPUnR0NPHx8ZhXaYiRSd7cepqclEB06BmSE14AYGBU\ngALmpYl99JfaR6PRYG79MfnyKMZXJbyIJurGKaKjo3M9WenQoQNXr17l8uXLqYpQajQaOnTooPdY\nJiYmrF69mmnTpjFmzBi13L6Pj4/OHkOv7jKfEW3dFyFE9pNkRXzQjEzMMCpULM/OX9zehdjHdwGF\nAsXLE/PXhVR9NIb58jTGt4WXlxfHjx/n+PHjqR5zdHTM8joRCwsLFixYkGGfsmXLqnc9ZiStS0NC\niOwjyYoQecggnzEFS/9vQWeB4uWI/eeW+rMmnzHGZqXzIrS3Tr58+fjxxx8JCAhQi8IVL16cZs2a\n4e7unqU1K0KId4skK0K8RfIXscC8ahNePLyJQT5jTMvYYpDPKK/DemsYGBjQqVMnOnXqlNehCCFy\nUbrJSkREBPv378fQ0JBPPvkk1beWpKQkNm7cSFJSEq1bt87ytupCiLQVKFaWAsXK5nUYb40///yT\nOXPmqHdH2dvbM3ToUOzs7PI4MiFEbkl33nTjxo1Mnz6dGzdupDm9amhoyLVr15g+fTqbNm3S+4R7\n9+7lyy+/pHnz5tjb2+Pq6sqcOXN4/vy5Tr/o6GjGjRtHw4YNcXBwoHfv3uomii+Lj49n5syZODo6\nYm9vj6enJ2fPnk3VT1EUfH19cXZ2platWnh4eLBv3740Y9y8eTNt27alZs2auLq6snHjRr2fnxAi\n+9y4cYNPP/2UEydOEB0dTXR0NMeOHeOzzz5L8/1ACPF+SjdZOXbsGABdunRJ9+CuXbuiKApHjx7V\n+4QrV67E0NCQr7/+mp9++omePXuyYcMG+vbtq9Ovf//+HD9+nPHjx7Nw4UISExPx8vLiwYMHOv18\nfHzYtm0bw4YNw9fXl5IlS9K3b19CQkJ0+s2bN4/Fixfj5eXFTz/9RO3atRk6dGiq2Ddv3syECRNw\ndXVl+fLltG3blkmTJknCIkQeWLZsGbGxsanuxomNjWXZsmV5FJUQIrelexno3r17ANjY2KR7sK2t\nrU5ffSxbtkxn48P69etjZmaGj48Pp0+f5uOPP+bAgQP88ccfrFmzRq1pULt2bVq2bMlPP/3EuHHj\nAAgJCWH37t3MmDFD3VCsfv36uLm5sWDBApYsWQKkFNtasWIF/fv3x9vbG0ipNPnXX38xe/ZsmjZt\nCqRc2po3bx4dO3ZUay80aNCABw8eMH/+fLp164ahoaHez1UI8WbOnDmDRqOhWbNmDBo0CEVRWLJk\nCUeOHOHMmTN5HZ4QIpekO7Py4kVK7YeMimZpH9P21UdaOzTXrFkTRVHUWZNDhw5RqlQpneJLhQoV\nokWLFgQFBaltQUFBGBkZ0bZtW7XN0NAQNzc3goODSUhIAODo0aMkJibi7u6uc153d3euX7+uJlvn\nz5/n6dOnqfp5eHgQGRnJuXPn9H6eQog39+TJEwCmTZtGrVq1sLe3Z9q0aQA8ffo0L0MTQuSidJMV\n7d4bBw8eTPdg7WPFir1ZDQjttydra2sgZX+OKv/X3p3HRVWvDxz/HFZRBCFFQVxSUFzSXND0mhqW\nll67mmWl1y13EZfK0H5plrmk5pa7SS6lopWGCWkiuSCmlGviAhomICrKIorAcH5/cOfEMKAMIKA8\n79eLl3LmLM85zJx5znd1dzdaz83Njbi4OC05ioqKwtXV1WhaeDc3NzIyMrhy5Yq2npWVFbVr1zZa\nT1VVbY4Q/b+5j+3u7m6wnhCiZOh0OsDwHqO/N2VlZZVKTEKIkpdvNVCLFi0IDAxk5syZ2Nra4uXl\nZfD6vn37mDlzJoqi0KJFi0IHEB8fz5dffkn79u1p3LgxAImJidp8HTnZ29sD2Y1vbWxsSEpK0pbl\nVKVKFW0/kD2TauXKlfNdTz/7qv7f3LOu6o+R1yytQohH76effspzFNncy00ZxVYI8fjIN1np27cv\ngYGBJCcn4+3tTZ06dbTZSKOiooiOjkZVVRRFoW/fvoU6+N27dxk9ejSWlpZa0a4QQuQ2adIkg9/1\nQ9vnXG7qkPtCiMdHvsnKc889x5tvvom/vz+KohAdHU109D9zluifZt544w3atWtn8oHv37/PyJEj\niYmJ4dtvv6V69X9G6bS3t8+zFCN3yYednR2xsbFG6+lLVPQlJ3Z2dqSkpOS7nr7kRL/f5ORkqlat\nanTcvEpxhBCPVlmbyFEIUfIeOILt9OnTqVq1KmvXriUtLc3gtQoVKvDOO+/g4+Nj8kEzMzPx8fHh\n7NmzfP3111pbFT03NzcOHz5stF1UVBTOzs7aRGNubm7s3buX+/fvG7RbiYyMxNLSUmuj4ubmRnp6\nOn///bfB9O2RkZEGbWX0bVMuXrxokKzo26rkjlMI8Wj9+9//lgkChRAPTlYURcHHx4eBAwcSFhbG\n1atXAXB1daVdu3aFKmlQVZX33nuPo0ePsmrVKpo1a2a0jpeXF9u3byc8PFybcv3OnTvs27fPoKeO\nl5cXX375JUFBQVrXZZ1OR1BQEB06dMDSMnuY8o4dO2Jubk5AQADe3t7a9gEBAbi7u1OzZvZooc8+\n+ywODg7s3LnToLToxx9/pEqVKrRs2dLk8xVCFN78+fNLOwQhRBlQoLmB7O3tefnll4vlgNOnT2f3\n7t2MHj2aChUqcPLkP7PM1qhRg+rVq9OlSxeaN2/OpEmTmDRpEpUrV2b16tUADBs2TFu/UaNGdO/e\nndmzZ5ORkYGrqyubN28mJiaGBQsWaOs5OjoyZMgQVq9eTaVKlWjcuDG7du3i6NGjrFixQlvPwsKC\n8ePH8+mnn+Lk5ET79u0JCwtj+/btTJ06FQsLmUpJCCGEKGmF+vb18vLCzMyMvXv3mrztwYMHURSF\nlStXGo1A6e3tzdixY1EUhdWrV/P555/zySefkJ6eTosWLdi4caNB2xaAOXPmsHDhQhYvXkxKSgoe\nHh6sXbtWG7BO791336VSpUps2LCBmzdv8vTTT7N48WI6depksN5bb72FmZkZfn5++Pn54ezszLRp\n03jrrbdMPlchhBBCFF2hkpXY2NhC1yM/aNyWnOzs7Jg5cyYzZ8584HpWVlb4+vri6+v7wPUURWHU\nqFGMGjXqocfu27dvoXs4Pc6uXLnC3bt3adiwobQTEEIIUWZIvYZAVVW++OILbZ4kNzc3ZsyYQaVK\nlUo5MiGEEEKSlSdCXFyc0azVprh48aLBhI6RkZF8++23RgMBFkalSpVwdnYu8n6eZGm3YkhLuIK5\nVUUquTTEzLJCaYckhBBliiQrj7mkpCRGjhxZ5KHHzcwMZ17YuXMnAQEBRdqnfr8bN26UMWrykZbw\nN0kXw7Tf0xOv4disq1TDCSFEDoVKVs6dO1fccYhCsre3Z9WqVUUqWUlNTeXTTz/VJn4EaNmyJRcu\nXMDa2pqXX35Z60JuqkqVKpXpRCXzbnKpHv9u3AWD3zPvJZF24y8sKpbNa1ba1wuyJzcMDw8nMTGx\nXLYtE6I8KlLJyqVLlwgICOCnn34qVM8gUTyKWs1y48YNMjMzDZYdP34cyE5ktmzZwvPPP6+NR/Mk\nuHPnDgCJkUdKNQ5FUQxKUVRVJTHyt1KMqGD016+krV+/ngULFpCenq5N9dG7d28uXrzIggUL6Nq1\na6nEJYR4tExOVm7dusVPP/1EQEAAf/7556OI6Yl3/fp1kpNL/wlV7/z58w8c0jwrK4v9+/fTpk2b\nEozKmJ2dHU5OTsWyL1tbWwCquD2HRUW7h6z96Ojup5J8+XfUzHQAbKrWoaJzA4N1sjLuk3EnATMr\nGywrOZRGmJrMu8kkRh7Rrl9JCgkJYfbs2UbL33zzTaZPn87evXslWRHiCVWgZOX+/fv88ssvBAQE\ncPjwYXQ6ncGXm9SvF9z169cZNXIk6TmqXMqC3E/4uW3atIlNmzaVYETGrCwtWblqVbElLAAWFe2w\ntHUstv2ZytLWEWsHZ9KTb2BuVdGo+ic9JYHEi4chSweATfX62D3dqjRCLXV+fn7aLO9//PGHtvxf\n//oXAGfOnCmt0IQQj1i+yYqqqoSFhREQEMAvv/zC3bt3teV6iqIwYsQIbah78XDJycmkZ2TwvJkZ\n9mUoyUtSVS4AaUBN4D5wBTAHGgC1zc1LMbrs+A5mZJCcnFysyUpZoJhZYF0l76q81NgILVEBuBd/\niUo1G2FuVbGkwiszzp49C8DChQsNBnPUV4PGx8ebtL9r164xa9YsDh8+jKqqtG/fng8//PCh1aqn\nT59my5YthIeHEx8fj4ODA61atWLChAm4urqaeFZCiILIN1np1KkTN27cAAwTFBcXF5599lkCAwMB\nmDhx4iMO8clkryhULUPJSlVFoX6uZaqqSqlZadNl5lqgoup0ea76pNM3AHdwMKwKS0hIADBqd/Ug\naWlpDBw4EGtra+bOnQtkJ0GDBg0iICCAChXy7z4eGBhIVFQUAwcOpEGDBly/fp1ly5bRp08fAgIC\njEbZFkIUXb7JyvXr14Hs0pM6derQtWtXunbtyjPPPMPFixe1ZEU8uSRRKX021euTnnxd+93KvjoW\nNpVLMaLS4+zszJUrVzh48KC2TKfTsWjRIgCTGoD7+/sTExPDzz//rM3E3qBBA7p168aWLVsYPHhw\nvtsOHz4cR0fDqsMWLVrQpUsXtm7dWqiZ6IUQD2b2oBf1X1ZOTk5Ur15dnhjKkShVZX9WFieyskh/\nQONb8WhVeKoWDo06Y1PDncp1nqVKg3+VdkilxsvLC1VVGT9+vLbsueeeY8eOHSiKwgsvvFDgfYWE\nhNC8eXMtUYHs2eRbtmxJcHDwA7fNnahAdomzo6OjyVVRQoiCybdkxcrKivT07B4K4eHhhIeHM2vW\nLJo1a0azZs1KLEBRsu6qKr+qKjdzLLuuqnSVUpZSY2XvhJX9k9VOpzBGjRrFnj17iImJ0R6kUlJS\ngOxSlREjRhR4X5GRkXTp0sVouZubG7t37zY5tqioKBISEnBzczN5WyHEw+VbsnL48GFmzJihdVdV\nVZWsrCxOnjzJxo0btfX8/f21G4Z4/IXkSlQArgE3ijhCrhBFZW9vj7+/P3369NFKN5566in69OnD\nli1bTBp8MDExMc/17e3tTR5WQKfT8fHHH2uxCCGKX74lK7a2trzxxhu88cYbXLt2jYCAAHbu3MnF\nixeBf6qIpk+fzqxZszh58mTJRCwemVRVJSGf1/YDr6kqZlLCIkpR1apVHzoTe0n75JNPOHHiBGvW\nrKFy5fLZnkiIR+2BbVb0atSowYgRI9i5cyc7duxg8ODBVK1aFVVVUVVVqy4SjzdrwDKf1+4C1/N5\nTYjHjb29PUlJSUbLk5KSsLMr+CCB8+fP57vvvmP27Nm0a9euOEMUQuRg8gi2Hh4eTJ48GV9fX8LC\nwtixY8dDG6SJx4OFotAWOKKq5NUJ1KakAxIih6ZNmz50nYIODOfm5kZkZKTR8sjISOrXz92JP28r\nVqxg7dq1TJ06lZ49exZoGyFE4RSoZCUviqLg4eFBt27d+PTTT4szJlGK6ikKrysKLwI5B1RvBGVq\nEDtR/mRmZj7wR2fC+DNeXl6cPHmSq1evasuuXr3K8ePH82x4m9uGDRtYvHgxEydOpF+/foU6HyFE\nwRVpIsM///wTb29vzMzM6NGjR3HFJEqZlaLgoih0zsriDyALcJZERZSyFi1aGIz9o9PpiImJ4ebN\nm9jY2NC4ceMC76tv375s2rSJMWPGaF2hlyxZgouLC2+++aa2XmxsLC+++CJjx45lzJgxAOzatYvZ\ns2fTsWNH2rZta9Bez9bWtsAlM0KIgitSsqL3oEnwxOMpXVXZA+hbI11TVboCNSRpEaVk8+bNRstU\nVWXdunXMnTuXIUOGFHhfNjY2rF+/nlmzZuHr66sNtz9lyhRsbP6p8NS3y8t5jzt06BAABw8eNBig\nDsDT05MNGzaYempCiIcolmRFmC6xjCd451WV3M2mz6lqqb1hyvr1EqVDURSGDBnCl19+yYoVK3jx\nxRcLvG2NGjVYsmTJA9epWbMmERERBstmz56d5+zPQohHR5KVEnbnzh0ADpXxcUvymoX5r6ws/iqd\ncDT66ycEQFZWFvv27ePu3bt5NpgVQjwZJFkpYba22c1WO5iZUaWMVKnE/2/G5UygFpAE5B40vBLQ\n3swMy1KKOVFVOZSVpV2/J1FGaiKpMWfJyriPjdPT2FSrW9ohlSl59QbSN6pVFOWhsyULIR5f+SYr\njRo1Ksk4yp0qZWTW5Tuqyh+AvpLlApDXDFAvAvZmhe48Jh4iKzOD2xG/omZmV75lpNxAMbeggqNr\nKUdWdjxsVuWhQ4eWUCRCiJKWb7KiqiqKojyw8azMyvv4i1dVcv+FrQEr/mlc68aTm6hk3DNtaPVH\nJT35hpao6N2Lv4S5VcVSiihvpXm9cvcGguw5zFxcXOjZs6cMyibEE+yB1UAP6+UjvYAef3fzWFYZ\naK8oxJJd/VPtCUxK7ezssLKyIunikdIORWOWKyG8nxhH2u3YUoomf1ZWViaN8lpc8uoNJIQoH/JN\nVs6dO1eScYhScj+PZZXIHmulbgnHUpKcnJxYuXKlyZPWPUq7du0iJCSErKwsVFVlzJgxuLu7l3ZY\nRuzs7HByKtlZoNPT07VRYpcvXy5jmQhRzkgD23LOWVE4m6OETKH8DADn5ORU4l+6DzJ+/HgGDhzI\n2bNnmT17Nu7u7ri5uZV2WGWClZUVCQkJpKamUqtWrdIORwhRwp7MhgiiwGoqCm0UBTvAAegAXFZV\n9mRl8UdWFhlS1VeiHBwcqF49rybO4rnnngPg/PnzpRyJEKKkFUtvIEVROHv2bLEEJEpWlqrSAPD4\nX3uJsKwsLv7vtWtk9xbqWE5KWsoaVVX5/fffiY2NpXXr1uW+a+4777xDeHg477//Pu+++y6NGjXC\n2traYB1J9IR4MhWpN5AovKQycF3/+t/4KlmAq6rSBLiUa51o4EZWVqn3/CoL16uk+fv7c/ToUQC+\n/vprpk+fTrNmzUo5qtLTr18/FEUhKSmJCRMmGL0uD01CPLlM6g2k/8IqDwnMtWvXmDVrFocPH9bm\nDfnwww+L/HRrZ2eHlaUlBzMyiinSwsvZ++QK2SPU5h65NktV2VVG/t5Wlpal0gultBw7dkz7f2Zm\nJj/88EO5TlagfNx7TPFrz55kpqQ8dD2LypVLIBohHh2TegN5eHigKMoT31MoLS2NgQMHYm1tzdy5\ncwFYuHAhgwYNIiAggAoVKhR6305OTqxctarUe6EcO3bMqCuomZkZqqpiaWlJRkYG1tbWDBgwwKTZ\nbB+l0uiFUhIuX77Md999R2pqKl27dtXOMfcXs3601vJK3xtI/CMzJYUXf/21tMMQ4pGT3kB58Pf3\nJyYmhp9//lnredCgQQO6devGli1bGDx4cJH2XxZ6odja2uLv709WrjmK9KUqbm5uNG/enG7duhm1\nCxDF586dO/zf//2fNufRH3/8wciRI4Hs4eXPnDkDZCeS5f3Let68eaUdghCilEiykoeQkBCaN29u\n0EXS1dWVli1bEhwcXORkpSyoUaMG77//Pps2bSIuLs7gqT0jI4PIyEgiIyNJTExk/PjxpRjp4yMu\nLo7U1FSTtjlx4oTR5IyhoaEAeHl58cwzz3Djxg2aNm2Ko6NjoSfrq1Sp0mPZQNfLywszMzP27t1b\n2qEIIUqRJCt5iIyMpEuXLkbL3dzc2L17dylE9Gh06NCBDh068NVXXxEQEJDnOgcPHpRkpQCSkpIY\nOXKkUUlVQeQeufbUqVMALFq0SFu2Z8+eIsVnZmbGxo0bsbe3L9J+SlpsbGypN+4WQpS+fJOVpUuX\n5rtRXq+NHTu2eCIqAxITE/O8qdvb25d6W5NHoV+/fiQlJREaGkpGRobBl0PFimVrbpqyyt7enlWr\nVplcsgLw/fffaw25a9euzciRI9HpdMU6w3SlSpUeu0RFCCH0Hpis5H6i0f++bNkyo/WfpGTlcVOY\n6ofc/vOf/+Dk5MS2bdsMltvZ2RW66gEe3+qHwijsefr6+nL9+nVSU1N5+umnizkqIYR4/BVpIkO9\nJ62Y1t7enqSkJKPlSUlJZa7rbFGqH/KSu+tydHR0nmNaFNTjWv1Q0kq7wXVZV5BBKmWcFSGeXPkm\nK+W5pMTNzS3P0oTIyMgyN4FaUaof8hIUFMS+ffvQ6XTUrFmTESNGULkIYzRI9YMoDjK+ihDlmyQr\nefDy8mLevHlcvXoVV1dXAK5evcrx48d5//33Szk6Y8VZzeLj48OgQYNITEykdu3axbZfIYQQorCk\nN1Ae+vbty6ZNmxgzZozWE2bJkiW4uLjw5ptvlnJ0j56dnV2Zq+4S5duTPhClEOLBZNblPNjY2LB+\n/Xrq1q2Lr68vH3zwAbVr12bdunXY2NiUdnhCCCFEuSIlK/moUaMGS5YsKe0whBBCiHJPSlaEEGWW\ni4sLLi4uj2Tf165dY9y4cbRu3ZpWrVrh4+NDXFxcgbZNT0/n888/p0OHDjRv3py33nqL8PDwRxKn\nEEKSFSFEGbZv3z6Cg4OLfb/6yUovX77M3LlzmTdvHn/99ReDBg0iLS3todtPmTKF77//ngkTJrBq\n1SqqVavG0KFDpW2NEI+IVAMJIcqdokxWeu7cOXbt2sWcOXPo1asXAJ6envTo0YMlS5awfPnykjgF\nIcoVKVkRQpQ7D5us9EGCg4OxtLTklVde0ZaZm5vTo0cPDh06REZGxiOLW4jySpIVIUS5ExkZibu7\nu9FyNzc3oqKiHrhtVFQUrq6uWFtbG22bkZHBlStXijVWIYQkK0KIcqgok5UmJSXluW2VKlW0fQsh\nipckK0IIIYQo06SBrRCi3CnKZKV2dnbExsYaLdeXqOhLWAorbPBgUv/6q0DrWhRh3i4hHieSrAgh\nyp2iTFbq5ubG3r17uX//vkG7lcjISCwtLYs8p1a7deuKtL0QTyKpBhJClDteXl6cPHmSq1evasv0\nk5V26dLlodtmZGQQFBSkLdPpdAQFBdGhQwcsLS0fWdxClFeSrAghyp2+fftSs2ZNxowZQ3BwMMHB\nwXh7extNVhobG0vjxo0Nxk5p1KgR3bt3Z/bs2Wzbto2wsDAmTpxITEwM48aNK43TEeKJJ9VAQohy\nRz9Z6axZs/D19UVVVdq3b8+UKVMMJitVVVX7yWnOnDksXLiQxYsXk5KSgoeHB2vXrsXDw6OkT0WI\nckGSFSFEuVSQyUpr1qxJRESE0XIrKyt8fX3x9fV9VOEJIXKQaiAhhBBClGmSrAghhBCiTJNqoGKi\n0+mA7GnnhSjv9J8D/edCyD1CiJxMvUdIslJMbty4AUD//v1LORIhyo4bN25Qp06d0g6jTJB7hBDG\nCnqPUNTczdxFoaSlpXHmzBmqVauGubl5aYcjRKnS6XTcuHGDpk2bUqFChdIOp0yQe4QQ/zD1HiHJ\nihBCCCHKNGlgK4QQQogyTZIVIYQQQpRpkqwIIYQQokyT3kBl2Pbt25kyZQp2dnYEBwdTOcd08Dqd\njiZNmjB27FjGjh1bbMfSs7GxwcHBgcaNG9OjRw9eeeWVPLe7ffs2fn5+hISEEBMTg6qq1KpVixde\neIGBAwdStWpVIHvyt9jYWIP916pVi759+/Lf//63yPGXNYW5nnItn2zXrl1j1qxZHD58WBve/8MP\nP8TZ2fmh26anp7Nw4UJ27txJSkoKjRo14v3336d169YlGsvp06fZsmUL4eHhxMfH4+DgQKtWrZgw\nYQKurq6FiqUo8eS2evVqFixYQKtWrfj2229LJZaoqCiWLFnCb7/9xr1793B2dqZ///4MGDCgxOOJ\ni4tj0aJFHD16lFu3blGjRg1eeeUVRo4caTCtREHFx8ezevVq/vzzT86dO0daWhr79u3DxcXlodsW\n9T0sycpjICUlhTVr1vDuu+8+0uMoisKSJUuoXr066enpxMbGsn//ft577z22bt3KqlWrsLKy0taP\njIzknXfeQVEUBg4cSJMmTQCIiIjA39+fy5cv8+WXX2rrP//88/j4+ABw584dQkJC+Oyzz8jMzGTw\n4MGP9NxKgynXU67lky0tLY2BAwdibW3N3LlzAVi4cCGDBg0iICDgob0hpkyZwsGDB/nggw9wdXXl\n22+/ZejQofj7+5s8H1FRYgkMDCQqKoqBAwfSoEEDrl+/zrJly+jTpw8BAQFUr17dpFiKGk9Of//9\nNytWrNCS+sIoaiynT59m8ODBtG3blpkzZ1K5cmWio6NJTU0t8Xju3bvH4MGD0el0TJgwAWdnZ06f\nPs2SJUu4cuUKCxYsMDme6Ohodu/eTZMmTWjdujWhoaEF3rbI72FVlFk//PCD2rBhQ3Xo0KHqs88+\nqyYkJGivZWZmqg0bNlS//PLLYjuWh4eHeuXKFaPX9uzZo3p4eKgzZswwOP7LL7+sdu3aVb1165bR\nNjqdTv3111+131944QV10qRJRuu9/fbbat++fYvlHMoSU66nXMsn37p169TGjRsbvB/+/vtvtXHj\nxurXX3/9wG0jIiLUhg0bqtu3b9eWZWZmqt26dVNHjx5dorHkvAfpxcTEqB4eHuqSJUtMjqWo8eT0\nzjvvqNOmTVP/+9//qv369SvxWLKystTu3burPj4+hTp2ccdz6NAh1cPDQw0NDTVYPn/+fLVJkyZq\nWlpakWLbunWr6uHhocbExDx03eJ4D0ublTJOURRGjx4NYDBNfX5OnTrF4MGDadGiBS1atGDw4MGc\nOnWqSDG89NJLdOnShW3btnH//n0A9uzZw+XLl3n//fdxcHAw2sbMzIxOnTo9dN+2trZkZGQUKb7H\nTe7rKdfyyRcSEkLz5s2pVauWtszV1ZWWLVsSHBz8wG2Dg4OxtLQ0qDo0NzenR48eHDp0yOS/eVFi\ncXR0NFrm4uKCo6Mj8fHxJsVRHPHo7dy5k4iICN57771CxVAcsRw5coRLly4Va8lmUeLRvy9shyYn\nLQAAGspJREFUbW0NlleuXJmsrCyjmcQfpeJ4D0uy8hhwcnKif//+bN26lbi4uHzXO3fuHAMGDCAl\nJYW5c+cyd+5c7ty5w4ABAzh//nyRYujUqRPp6emcPn0agLCwMCwsLOjYsWOB96GqKjqdDp1OR3Jy\nMjt27ODw4cP06NGjSLE9jnJeT7mWT77IyEjc3d2Nlru5uREVFfXAbaOionB1dcXa2tpo24yMDK5c\nuVJiseQXX0JCAm5ubiZvWxzxJCcnM2fOHD744APs7OwKFUNxxPLHH38A2VU3b775Jk2bNqV9+/Z8\n9tln2kNeScbTvn176tSpw7x584iKiuLu3buEhYWxYcMG3n777RIdrLE43sPSZuUxMXz4cPz9/Vm6\ndCkzZ87Mc53ly5djbW3N+vXrtWy6Xbt2dOnShWXLlrFkyZJCH9/Z2RlVVbUhw+Pi4nBwcDB68z3I\nzp072blzp/a7oii88cYbDB06tNBxPa70jeNu3Lgh17IcSExMxN7e3mi5vb09ycnJD9w2KSkpz22r\nVKmi7bukYslNp9Px8ccf89RTT9GnTx+Tti2ueD7//HOefvppevXqVajjF1cs169fR1VVJk6cyIAB\nA3j//fc5c+YMixcvJj4+3qDNWUnEY2VlxaZNm/Dx8dEeYvT3ialTp5ocS1EUx3tYkpXHhL29PUOG\nDGH58uUMHz7coFhQLzw8nM6dOxsU+9na2uLl5UVISEiRjq8vMlQUpdD76NSpE+PHj0dVVe7du8fp\n06dZunQpFhYWTJs2rUjxPW6Kej3lWoqy4JNPPuHEiROsWbPGoLdiSQkPDycgIIAdO3aU+LFzU1UV\nRVH4z3/+o/XQ9PT0JDMzkwULFnDp0iXq1atXYvGkp6czfvx4EhISmD9/PjVq1NDuE2ZmZkyfPr3E\nYikOUg30GBk8eDB2dnb5lpAkJSVRrVo1o+VVq1Y1+Ykpt2vXrqEoirZ/Z2dnbt++bVLxpr29PY0b\nN9Zakg8ZMoQxY8awefPmQhU/P870M45Wq1ZNrmU5YG9vT1JSktHypKSkh1Zd2NnZ5bmt/mlU/3Ra\nErHkNH/+fL777jtmz55Nu3btTIqhuOL5+OOPef3113FyciIlJYXk5GStejQlJYX09PQSi0X/d2jf\nvr3B8g4dOqCqKufOnTMplqLGs23bNsLDw1mzZg3//ve/tfvE5MmT8ff3L3LTAFMUx3tYkpXHSMWK\nFRkxYgQ///wzERERRq/b29tz8+ZNo+U3b94scl1uSEgI1tbWNG3aFMiuXtLpdBw4cKBI+9XXc1+4\ncKFI+3nc5Lye7dq1IzMzU67lE8zNzY3IyEij5ZGRkdSvX/+h2169etUomY2MjMTS0pLatWuXWCx6\nK1asYO3atXz00Uf07NnTpOMXZzxRUVFs2bIFT09PPD09adOmDX/88QcnTpygTZs2bNmypcRiKWyb\nnUcVz4ULF7CzszMqhX/mmWdQVbVEH2qK4z0sycpjpl+/flSvXp1FixYZVSF4enqyf/9+7t69qy27\nc+cO+/bto23btoU+5u7duwkJCeHtt9/W2lV07dqVunXrMn/+fG7dumW0jU6nY//+/Q/dtz67z6uX\nwZMq9/Xs2rUrTz/9tFzLJ5iXlxcnT57k6tWr2rKrV69y/PhxunTp8tBtMzIyCAoK0pbpdDqCgoLo\n0KEDlpaWJRYLwIYNG1i8eDETJ06kX79+Jh27uOPZuHEjGzZsYOPGjdqPh4cHDRo0YOPGjXTr1q3E\nYunYsSOWlpYcOnTIYPmBAwdQFIVnnnnGpFiKGk+1atVITk7m77//Nlh+8uRJFEUp1Jg4hVUc72Hz\n6Y9bxVU5cu7cOYKDgxkwYIDWOMnc3JxKlSqxfv16FEWhTZs2tGnTBoB69eqxZcsWDhw4QJUqVYiK\nimLatGlcv36defPm8dRTTz3wWHv37qV169akpqZy9epVwsPDWbVqFcuWLaNDhw7MmDFDm9rezMyM\ndu3a8f3337NlyxZ0Oh3p6elcvXqVvXv38tFHH3Ht2jW6d+8OwPr167GxsaFOnTrEx8cTHR1NYGAg\nK1euxN3dnYkTJxapPUxZY8r1lGv55GvYsCGBgYHs3r0bJycnLl++zMcff4yNjQ2fffaZdrOOjY2l\nbdu2KIqCp6cnkP2lc+nSJTZt2kSVKlVITk5m/vz5nD59mvnz55s8CFpRYtm1axfTpk2jY8eO9O7d\nm/j4eO0nNTW1UIlyUeKpWbOm0c+uXbuwsrLCx8fHqNvuo4ylQoUK6HQ61q1bp5UgBAUFsXz5cl59\n9VVee+21Er8233//PcHBwdja2pKUlMTPP//M4sWLadiwIePHjzc5Hsh+2IqKiuL333/nzJkz1K1b\nl9jYWG7fvk3NmjUf2XtYGtg+hl577TW++uoro+5eDRs2ZMOGDSxatIjJkyejqiotWrTgm2++oUGD\nBg/dr6IoTJgwAQBra2scHR1p0qQJixYtomvXrkbr169fnx9//BE/Pz927NjBsmXLUFWVOnXq0K1b\nN6PhpQ8dOqQ9dVhZWeHi4kL//v0ZPnw4ZmZPXiGfKddTruWTzcbGhvXr1zNr1ix8fX21YdOnTJli\nMOy5qqraT05z5sxh4cKFLF68mJSUFDw8PFi7dq3Jo9cWNRb9e+7gwYMcPHjQYL+enp5s2LChROPJ\nT2GT9aLGMnbsWGxtbdm8eTN+fn5Uq1aN4cOHa2NllWQ8NWvW1HqQLl68mNu3b1OjRg3eeustRo0a\nVah4AMaPH69dX0VR+PTTT4F//v6P6j2sqCU5MowQQgghhInkEUwIIYQQZZokK0IIIYQo0yRZEUII\nIUSZJsmKEEIIIco0SVaEEEIIUaZJsiKEEEKIMk2SFSGEEEKUaTIonCiwpUuXsnTpUoNlFhYWODk5\n0a5dO3x8fKhRo0YpRSfEkymvz11OvXv3Zvbs2Sbv18vLi9jYWGrWrElwcHBRQjTZ0aNHGThwoNHy\nSpUq4e7uTt++fQs14mtBxcTEaMPV57x++pGnAV588UWjAcv018zFxYV9+/Y9svjys337dqZMmWKw\nzMLCAkdHR1q1aoW3t3eh5yiKiYlh+/btAAYjo5cVkqwIk+UcHVKn0xEXF8f3339PWFgYu3btMhhZ\nUQhRPPIblbUoUyuU9rQMuY9/9+5dTpw4wYkTJ7hw4QKTJ09+pMfW/+hFRESwdOlSFEXB1dXVKFnR\nr1/ao0TnvgffuHGDoKAgQkNDCQgIKNRDY0xMjHbuQJlLVqQaSBSKt7c3ERER7Nq1C2dnZwDi4uJK\n/AlNiPJE/7nL+TNr1qxC768sDGDu6elJREQEp06d0ko4FEVh48aNxMbGPpJj1qxZk4iICM6ePWvS\n9QsODiYiIkIrfSlNvXr1IiIigpCQEBo3bgxASkoKP/74Y6H2VxbeCw8iJSuiSOrVq0fXrl1Zt24d\ngMHNJT4+nuXLl3Po0CHi4+OpWLEizZs3Z+TIkbRu3Vpb7/bt2yxZsoSDBw9y8+ZNzM3NqVatGk2a\nNMHHx4e6desCaE85np6eDBs2jEWLFhEVFUXVqlXp168fw4YNM4jt/PnzrFq1iqNHj5KYmIitrS3P\nPvssw4YNMzh+zmL2ZcuWcejQIfbs2cP9+/dp3rw506ZNo06dOtr6e/bsYf369Vy6dIk7d+5gb29P\n3bp16dKlC0OGDNHWi4qKYuXKlfz222/cunULOzs7Wrdujbe3Nw0bNiyeP4AQOQQGBrJt2zYuX75M\nYmIiOp2O6tWr869//Ytx48Y9cDJTgPv377N06VJ++eUX4uPjAXjqqado3Lgxw4YNo1mzZtq6O3fu\nZMuWLZw/f5779+/j4uLCyy+/zOjRo6lQoYLJsVtaWtKrVy/8/Py4cOECWVlZnDlzBhcXFwDCw8NZ\nu3YtJ06cICUlhSpVqtCmTRtGjhxp8Hm6evUqS5Ys4dixYyQkJGBtbU2NGjVo2rQpkyZNwtHRMc9q\noAEDBnDs2DEURUFVVSZPnqyV7MyZM4devXoZVZ0FBwfj7e0NwHvvvcfw4cO1OObNm8fatWuB7MlH\n27Zti6qqbNq0ie3btxMVFUVWVha1a9emd+/eDBo0SJso1hQ1atSgV69e/Pnnn4DhPTguLo65c+dy\n7tw5EhISuHv3Lra2tjRp0oShQ4fSvn17ACZPnsyOHTu0c895Txw7dixjx44FsmeQXr9+PWfOnCE1\nNRUnJye8vLzw9vbGwcHB5NhNIcmKKLKcGbn+Znjp0iX69etHYmKiVqyYkpLCwYMHCQ0N5YsvvuCV\nV14BwNfXV5tGXS86Opro6GheffVVLVmB7CeuCxcuMHr0aO24sbGxzJ8/n3v37uHj4wPAkSNHGDFi\nBOnp6dp+k5KS+PXXXzlw4ABz587l3//+t8F5KIrClClTSElJ0ZaFhoYyevRodu3ahaIonDp1igkT\nJhicc0JCAgkJCaSlpWnJSnh4OMOGDdNmX4XspGzPnj3s378fPz8/WrVqVcgrLkTefvvtN44cOWKw\nLCYmBn9/f44dO0ZAQAAWFvnf9ufMmcPmzZsNPosxMTHExMTQtm1bLVmZMWMG3377rcF6V65cYdWq\nVRw+fJhvv/0WKyurQp1DXk/4P/74I1OmTCErK0s7ZkJCAoGBgezdu5e1a9dqM/yOHDmSqKgobb2M\njAwiIyOJjIxk6NChBjND566Gyvl7zsn68lunc+fOPPXUU9y6dYtdu3YZJCtBQUEoikKtWrW0RMXb\n25t9+/YZ7CMyMpK5c+dy7NgxVqxYYdrF+p+87sEA169f1+LQS0pKIjQ0lCNHjvD111/Tpk0bo+qw\nvP7v5+fH3LlzDV6Li4vjm2++Yf/+/fj7+xdq1u2CkmogUSRRUVH88ssvAFSsWJEXXngBgJkzZ5KY\nmIidnR0bNmzg1KlT7N69m3r16qGqKjNmzCAzMxPI/mJXFIWXXnqJ8PBwfv/9dwICAvD19aV69epG\nx0xOTmbixInak5b+KW7NmjXcvn0bgI8//piMjAwUReGTTz7h999/Z+nSpVhYWGjHT0tLM9p35cqV\n+fHHHzl48CD16tUD4PLly5w6dQqA33//naysLAD8/f05c+YM+/fvZ+XKlQbJz9SpU7WnzR9++IHT\np0+zfft2HB0dSU9P12YqFcIUS5cuxcPDw+AnZ9Vrz5492bp1K0eOHOHPP/8kNDSU3r17A9nv4/37\n9z9w//rPYvPmzQkLC+PEiRMEBQXx8ccfU79+fQBOnjypJSq9e/cmNDSUEydOMGnSJADOnDnDpk2b\nTD63jIwMtm/fzsWLF4HsL8lnnnmGe/fuMXPmTFRVxcLCgmXLlvH777/zySefaNtNmzYNgMTERC1R\nGTBgACdOnODo0aN89913jB8/nsqVK+d7/I0bNzJr1ixUVUVRFGbPnq1VFfXq1UtbL2diYG5uzquv\nvoqqqpw/f56oqCgg+z6hL+HQX//AwEAtURkxYgRHjx4lPDxca2j866+/Fqp6KS4uTqv6MTc35+WX\nX9Zec3FxYcWKFezfv59Tp05x/PhxLSHKysrSZsmePXs269ev1849Z3Wjt7c3165dY8GCBSiKwvPP\nP09ISAgnT57kiy++ALJLswqbaBWUlKyIQsndQ6FOnTrMnDkTR0dH7t+/z5EjR1AUheTkZAYMGGC0\n/e3btzl79izNmjXD1dWVCxcucPz4cZYvX46bmxsNGjRg0KBBeTYArF69uvYE0759e1588UV++ukn\nMjIyCA8Px93dnejoaBRFoWHDhvTt2xeALl260LlzZ/bu3UtycjLHjx+nXbt2Bvt+5513aNCgAQAd\nO3bUbj4xMTE0b94cV1dXbd1Vq1bRqlUr6tWrR7NmzejUqROQXSp0+fJlFEUhJiZGu1nldOHCBRIS\nEh5aLC9ETg96ygeoVq0aS5cuJTw8nBs3bmgPBHqXL19+4P5dXV25ePEiUVFRLFu2jAYNGuDu7k6f\nPn2wtLQEMOgF88MPP/DDDz8Y7ENVVUJDQxk8eHCBzuno0aN5NmQdMGAAzs7OhIaGkpycjKIodOrU\nCS8vLwD69u3L5s2biYiI4K+//uLvv//G1dUVOzs7UlJS2L9/PxUrVqR+/fp4eHgwatSoAsVjqtdf\nf52vv/4agJ9++onx48fz008/AWBmZqZ9/kNCQrRtVq1axapVqwz2o6oqhw4d4sUXXyzQcbdv3671\n3oHsv/20adO0+xdAlSpVOH/+PIsXLyY6Opp79+4ZHO9h7we9gwcPkpmZiaIoHDhwgM6dOxvFHhoa\nWqB9FZYkK6JQct4kVVUlLS2NjIwMAK2uPHfRYu7t9aUgn332GZMnT+by5cv4+flpTy4uLi4sX77c\n6EaWu6W7vk4bspOgW7duab/rG//mtW7O9fT0pSmQXVKkl56eDsBLL71E//79+e6779i3bx/79u1D\nVVXMzc156623mDp1KgkJCXlep9znn5iYKMmKMIm3t7fWfiC3O3fu8Pbbb3Pr1i2jKgz9Zyqv0sSc\nPvzwQxISEjh9+jTffPONtp2DgwNffPEF7du3N/jc5Pf+TkpKKvA55dyHjY0NDRo04PXXX+f1118H\neOjnOSIiAsiuFqpVqxbz5s1j+vTpWrWU/hzc3d356quv8iytLYr69evTvHlzTp48ya5duxg7diy7\nd+9GURTatWun3a8e5XVTVZX09HSDamfIrq7bunVrnlVa+vt2QRTknmZK7IUhyYooFG9vb0aNGsXu\n3bv54IMPiI+PZ+zYsezatQsHBwfMzc3JysqiTp06/Pzzzw/cV7NmzQgMDCQ2NpZLly5x7tw5li9f\nTlxcHPPnz+err74yWF/f8E8vZ4MyBwcHgwQgLi7OYN2cv+dVv5qzPj+/D+XUqVPx9fXl/PnzREdH\ns3PnTvbv38+mTZt49dVXDY7fvn17rZGdEI/SkSNHtESlXbt2zJ8/H0dHR7755hs+++yzAu2jVq1a\nbN26lZs3b3Lx4kUiIyNZvXo1N2/eZMaMGQQFBRl8bubNm2fU9stUnp6eWnVEXgr6edav16lTJ0JC\nQrQSzjNnzrBixQoiIyNZvny5Vn2Ul8J25X7ttdc4efIkf//9NytWrND+Dn369NHWyXndNm3aRIsW\nLQp1LL1evXoxc+ZMjhw5go+PD0lJSUyePJm6devStGlT4J92M1ZWVmzcuJGmTZty7949WrVq9dBS\nupxy/g0mTJjAyJEjixR7YUibFVFoFhYW9OjRg379+gHZYyTMnz8fa2trnnvuOVRVJTo6mnnz5nHr\n1i0yMjK4dOkSX3/9NYMGDdL2s3DhQkJCQjAzM6Nt27a8/PLL2Nvbo6qq0c0J4Nq1a6xZs4bU1FRC\nQ0O1el5LS0tat25NnTp1qFu3rlaPvHXrVu7evcu+ffu0olg7O7tC3SyOHTvGmjVruHTpEnXr1qVr\n1640b95cez02Ntbg+GFhYaxfv56UlBTS09M5d+4cS5cuZeLEiSYfW4gHyZloW1lZYW1tzcWLF9m4\ncWOB97F27VoCAwNJS0ujVatWvPLKKzg5ORl8FvXt0lRVZdGiRfzxxx+kp6eTlJTEgQMHeO+999i5\nc2exnVeLFi20+8GBAwcICQnh7t27bN26lbNnzwLZJaK1atUCsktqw8LCsLGxoUOHDrz00ktaY9+8\n7ic5ValSRfv/hQsX0Ol0BYqxR48eWtu5lStXAmBvb29QpaO/bvoYz507R0ZGBgkJCfzyyy+MGjWK\n8PDwAh1Pz8zMjPbt22sdC3Q6nUFXbHNzc1RVxczMDFtbW1JTU/n888/z3FfOc7906ZJWmgzQoUMH\nrb2fn58fBw8eJC0tjTt37nD06FGmTZvG6tWrTYrdVFKyIopszJgx/PDDD6SmphIYGMiwYcP48MMP\n6d+/P0lJSaxdu9aodKFmzZra/4OCgozqbwGtMVdujo6OLF68WGvcpV93xIgRWve5Tz75ROsNNG3a\nNK0BHmR/gKdNm1ao7pVxcXF88cUXBsfWq1ixotbDZ8aMGQwfPpz79+8ze/ZsoxFGy9qAS+Lx17Jl\nSxwdHbl9+za//vqr9l7M2ZvuYQ4dOkRYWJjR8pyfxWeffZZ+/fqxefNmYmJitIeVvNZ9mIKM7WFj\nY8NHH33E5MmTyczMZPTo0QbHsrKyMigt2bx5M998880DzyE/jRo1wtLSkszMTPz8/PDz8wOy2+nk\nrELOzdbWlq5duxIQEKC17ejZs6fWzgege/fuWinsn3/+adBoVx/f0KFDH3wx8tG/f382btxITEwM\nx48fJyQkhBdeeIGXXnqJbdu2ce/ePbp37w78837Ife3r1KmDg4MDiYmJBAYGEhgYCGQ3PPb09GTC\nhAl88cUXJCcnG/R60seu78L9qEjJijBJXkWFDg4ODB06VOujv2DBAurXr8+PP/7I22+/Te3atbGy\nssLOzg53d3feeOMNg5vLf//7X9q1a0f16tWxsrKiQoUKuLu7M27cOK2HQU7169dn9erVNG3aFGtr\na1xcXJg0aZJBXX7btm3Ztm0b3bt3p1q1alhYWFClShVeeOEFNm7cSI8ePYzOK69zy728SZMm9OnT\nBzc3N+zs7LShrr28vNiwYQNOTk5AdtH2999/T69evXB2dsbS0pIqVarg4eHBwIEDeffdd02/+KLc\nKkj1hJ2dHV999RWtWrXCxsaGGjVqMG7cOEaMGPHAtlM5X+vduzedO3fG2dmZChUqYGlpSZ06dRgy\nZIjBE/m0adOYN28enp6e2NnZYWlpibOzM8899xwffPABHTt2LNA5PahdW049e/Zkw4YNdO7cGQcH\nBywsLKhatSrdu3dn27ZtBuMmjRgxgtatW1O1alUsLCywsbGhSZMmfPTRRwaN/fNqx1G9enXmzp2L\nm5sb1tbWeY5Wm1/Mffr0MTinnFVA+u1WrFjB1KlTefbZZ6lUqRLW1tbUrFmTjh07Mm3aNG1wt4dd\nt9xxW1paMm7cOG3ZggULAJgyZQpvv/02VatWpWLFinh5ebFu3bo8r72VlRWLFi2iSZMm2NjYGJ37\nsGHDWL16NR07dtT+BtWqVaNly5aMGzcuz44ExUlRy/qwdUL8j4eHB4qiPLSOWwghxJNFSlbEY0Vy\nayGEKH+kzYp4bOQ3oqQQQognm1QDCSGEEKJMk2ogIYQQQpRpkqwIIYQQokyTZEUIIYQQZZokK0II\nIYQo0yRZEUIIIUSZJsmKEEIIIcq0/wepor6aXsuuXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa88f4c46d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gene_summary('HLA-C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=113.0, p-value=0.0523450632732 (two-sided)\n",
      "{{{TAP1_plot}}}\n",
      "{{{TAP1_benefit:5511.17 (range 776.65-19455.27)}}}\n",
      "{{{TAP1_no_benefit:2196.74 (range 225.34-22238.25)}}}\n",
      "{{{TAP1_mw:n=26, Mann-Whitney p=0.052}}}\n",
      "TAP1 scaledTPM, Bootstrap (samples = 100) AUC:0.757985898532, std=0.100883829272\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>aa_change</th>\n",
       "      <th>hvar_pred</th>\n",
       "      <th>hvar_prob</th>\n",
       "      <th>hdiv_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, aa_change, hvar_pred, hvar_prob, hdiv_pred]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h/Pzzz9y/f7/Yg9i7dy8ajYbatWvrtM+cOZPXXnuNpk2bMnjwYE6fPq1zf3Jy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\n6VJq1KihXSeMjIz4/PPPiy2WwiDNQM+RYcOGYWVllWUNSUxMDFWrVs1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa88f3e3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gene_summary('TAP1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=98.0, p-value=0.257704686002 (two-sided)\n",
      "{{{TAP2_plot}}}\n",
      "{{{TAP2_benefit:3134.31 (range 482.16-14387.11)}}}\n",
      "{{{TAP2_no_benefit:2239.00 (range 331.00-13857.10)}}}\n",
      "{{{TAP2_mw:n=26, Mann-Whitney p=0.26}}}\n",
      "TAP2 scaledTPM, Bootstrap (samples = 100) AUC:0.641049351793, std=0.12846728123\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>aa_change</th>\n",
       "      <th>hvar_pred</th>\n",
       "      <th>hvar_prob</th>\n",
       "      <th>hdiv_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, aa_change, hvar_pred, hvar_prob, hdiv_pred]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tbV2QcYoC4OnpWdghCOTf4UlBQUF8//332mNVVWnRokWGfoqiUKZMmYIMTQhR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\nVltRuoyMjPD29mblypUYGRnh4eFR7iPsCiFERSCV4rnYtm0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9TJgwgX379qEo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\nyYoQQgghKjRJVoQQQghRof0/dXAYZEVwjzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa88f5d3510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_gene_summary('TAP2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>benefit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0040</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0471</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0522</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1233</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1249</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1849</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1994</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2131</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2278</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2389</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2849</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2937</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3529</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4072</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5037</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5122</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5338</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6229</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6428</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7577</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7729</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8728</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9517</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9723</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9881</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id benefit\n",
       "0        0040   False\n",
       "1        0471   False\n",
       "2        0522   False\n",
       "3        1233    True\n",
       "4        1249   False\n",
       "5        1849    True\n",
       "6        1994   False\n",
       "7        2131    True\n",
       "8        2278    True\n",
       "9        2389    True\n",
       "10       2849   False\n",
       "11       2937   False\n",
       "12       3529   False\n",
       "13       4072   False\n",
       "14       5037    True\n",
       "15       5122    True\n",
       "16       5338   False\n",
       "17       6229    True\n",
       "18       6428   False\n",
       "19       6800    True\n",
       "20       7577   False\n",
       "21       7729   False\n",
       "22       8728   False\n",
       "23       9517   False\n",
       "24       9723   False\n",
       "25       9881   False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_df[['patient_id', 'benefit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>benefit</th>\n",
       "      <th>pfs</th>\n",
       "      <th>progressed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0040</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0471</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0522</td>\n",
       "      <td>False</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1233</td>\n",
       "      <td>True</td>\n",
       "      <td>398</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1249</td>\n",
       "      <td>False</td>\n",
       "      <td>41</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1849</td>\n",
       "      <td>True</td>\n",
       "      <td>265</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1994</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2131</td>\n",
       "      <td>True</td>\n",
       "      <td>560</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2278</td>\n",
       "      <td>True</td>\n",
       "      <td>655</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2389</td>\n",
       "      <td>True</td>\n",
       "      <td>646</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2849</td>\n",
       "      <td>False</td>\n",
       "      <td>75</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2937</td>\n",
       "      <td>False</td>\n",
       "      <td>37</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3529</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4072</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5037</td>\n",
       "      <td>True</td>\n",
       "      <td>561</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5122</td>\n",
       "      <td>True</td>\n",
       "      <td>565</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5338</td>\n",
       "      <td>False</td>\n",
       "      <td>67</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6229</td>\n",
       "      <td>True</td>\n",
       "      <td>252</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6428</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6800</td>\n",
       "      <td>True</td>\n",
       "      <td>650</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7577</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7729</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8728</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9517</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9723</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9881</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id benefit  pfs progressed\n",
       "0        0040   False   20       True\n",
       "1        0471   False   61       True\n",
       "2        0522   False   58       True\n",
       "3        1233    True  398       True\n",
       "4        1249   False   41       True\n",
       "5        1849    True  265       True\n",
       "6        1994   False  121       True\n",
       "7        2131    True  560      False\n",
       "8        2278    True  655      False\n",
       "9        2389    True  646      False\n",
       "10       2849   False   75       True\n",
       "11       2937   False   37       True\n",
       "12       3529   False   64       True\n",
       "13       4072   False   61       True\n",
       "14       5037    True  561      False\n",
       "15       5122    True  565      False\n",
       "16       5338   False   67       True\n",
       "17       6229    True  252       True\n",
       "18       6428   False   61       True\n",
       "19       6800    True  650      False\n",
       "20       7577   False   60       True\n",
       "21       7729   False   60       True\n",
       "22       8728   False   61       True\n",
       "23       9517   False  121       True\n",
       "24       9723   False   19       True\n",
       "25       9881   False   84       True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_df[['patient_id', 'benefit', 'pfs', 'progressed']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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